jeff-adapter-trading-desk-gguf

The LoRA GGUF of jeff-adapter-trading-desk (Jeff v1.3), for llama.cpp.

Trading desk decisions. Applies a trading desk's written rules to the live order book - buy, sell or hold; size; venue; open orders; risk-off.

This repository holds only the adapter. Load it on top of the base GGUF from mstrasser/jeff-base-gguf; there is no separate full model per adapter.

It works on either base format: Q8_0 (recommended, within 0.4 points of full precision on every adapter test) or Q4_K_M (within 0.4 points).

Files

File	What it is
`loras/trading-desk.gguf`	The LoRA (169 MB), the same file for every base format. It stays at full precision; quantisation applies to the base weights only
`models/trading-desk.jeff.json`	What a client needs: the answer codes and their token ids, the prompt layout (`live-last`) and the temperature fitted for each format

trading-desk.jeff.json has a temperature for each format (temperature_by_format: f16, q8_0, q4_k_m); use the one for your base format, so the probabilities stay calibrated.

Results

The same test, through llama.cpp: the base GGUF plus this LoRA GGUF, with the temperature refitted for each format. Running Jeff with llama.cpp

Test set	Full precision	Q8_0	Q4_K_M
`test`	98.1% · 0.010	98.1% · 0.010	97.8% · 0.011

All results, and the same test at full precision against the untrained model and the base alone: jeffhub.ai/adapters/trading-desk.

How to run it

Use llama.cpp commit cb7934c52ca8710994b2ecc19775ebefcfdb8d01 or newer: it needs the qwen35 architecture and LoRA on the output layer.

hf download mstrasser/jeff-base-gguf --local-dir jeff-gguf
hf download mstrasser/jeff-adapter-trading-desk-gguf --local-dir jeff-gguf
cd jeff-gguf
llama-server -m models/base-q8_0.gguf -c 8192 -np 1 --lora-init-without-apply --lora loras/trading-desk.gguf

Jeff is not a chat model. For each decision you run one forward pass over the prompt and read the probabilities of the answer codes; you never sample text.

Build the prompt exactly as Jeff does (the question, the state, the options as answer codes, and the changing state field under "Latest", then the chat template with thinking off). The Jeff repository builds it for you (jeff.model.decision_messages).
Tokenize without a beginning-of-sequence token, with special tokens parsed.
Run one forward pass over the whole prompt from an empty state, with this adapter active. Clear the state between prompts: Qwen3.5 has recurrent layers, so a reused state would carry over.
Read the probabilities. Take the logits of the first N answer-code tokens (token_ids[:N], N = the number of options), divide by the temperature for this adapter and your format, and apply a softmax over those N.

With llama-server, each decision is one POST /completion with "n_predict": 1, "cache_prompt": false, a lora list that names every loaded adapter (scale 1 for this one, 0 for the rest; an adapter left out of the list keeps scale 1.0), a logit bias of +1000 on every answer-code token of the request, and n_probs set to the number of options. From the llama.cpp library: load the base once with llama_model_load_from_file, this adapter once with llama_adapter_lora_init, and per request call llama_set_adapters_lora with scale 1, clear the memory, decode once and read llama_get_logits_ith(ctx, -1).

The full guide, with an example prompt and request: Running Jeff with llama.cpp.

Data and licence

Adapter licence: Apache-2.0.

Qwen3.5-0.8B notice: these weights were modified from Qwen3.5-0.8B by the Jeff project: jeff-base is a fine-tune of Qwen3.5-0.8B, and this adapter was trained on top of it. Qwen3.5-0.8B is Copyright 2026 Alibaba Cloud and licensed under the Apache License, Version 2.0; a copy of that licence is in LICENSE.

To confirm: whether Apache-2.0 for the adapter is compatible with the FI-2010 licence once that is confirmed.

It was trained on:

FI-2010 limit order book benchmark. Licence: Creative Commons Attribution 4.0 (CC BY 4.0), as the Etsin record lists it (open licence) · Not made by a model

Ntakaris, Magris, Kanniainen, Gabbouj and Iosifidis (2018), "Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods", Journal of Forecasting 37(8). Published on Fairdata/Etsin (urn:nbn:fi:csc-kata20170601153214969115). Order books for five Nasdaq Helsinki stocks, 1 to 14 June 2010.

To confirm: the CC BY 4.0 licence, on the FI-2010 Etsin record
Synthetic desks, positions, orders and news. Licence: Released with the adapter under Apache-2.0 (made for this adapter) · Made by GLM 5.3 (own hardware), for the texts; everything else by code

400 desk definitions, venues, positions, open orders, parent orders and news events generated by code with fixed seeds. Desk rules, market context notes and news lines were rewritten by GLM 5.3 and checked by code.

mstrasser/jeff-adapter-trading-desk-gguf